Determining an accurate estimate of the value of a property is important to entities such as buyers, sellers, real estate agents, lenders, and financial institutions that participate in a real estate market. For example, accurate estimates enable sellers to formulate realistic expectations as to the values of the properties being sold and to set appropriate sales prices for those properties. Buyers, on the other hand, rely on accurate estimates to formulate offers for the purchase of properties. Lenders may use predicted property values to apply loan-to-value guidelines and lenders and financial institutions that buy and sell mortgages may depend on these estimates to determine the value of a particular mortgage or portfolio of mortgages and the risk associated with holding such mortgages.
There are several known methods for estimating the value of a property at a specified time, four of which are the hedonic or property characteristic methods, the repeat sales methods, the tax assessment methods, and the Neural Network methods. Hedonic methods estimate the value of a property by assigning values to attributes of the property and totaling the values. For example, a hedonic method may estimate the value of a home by assigning a value to the home style, the neighborhood, each bedroom, each bath room, etc.
Repeat sales methods of estimation use actual sales prices and time data points to estimate a market-level price index, wherein the present value of a property may be estimated using the prior sale information. For example, a repeat sales index model proposed by Bailey, Muth, and Nourse (the BMN model) specifies that the change in the logarithm price of a property over a known period of time is equal to a logarithmic price index plus and error term. Another repeat sales model by Case and Shiller (the Case-Shiller model) improves on the BMN method. Unlike the BMN model that assumes that the error term is independent, the Case-Shiller model assumes that the error term is a linear function of the time between sales.
Tax assessment methods rely upon historical tax assessment data to predict the value of a property or estimate a price index for a collection of properties. The tax assessment methods use the proportional relationship between assessed values in any given geographic area, such as metropolitan statistical areas (MSA's), counties, or zip code areas, and sale values to predict property values.
Neural Network methods predict property values using the same data and underlying principles as in the hedonic model. Neural Network methods use a network training process that forms multiple linear combinations of the property characteristic variables, passes these variables through “activation functions”, then forms a linear combination of these results that is then compared with the desired output, i.e., the observed property value. Thereafter, the coefficients of the linear combinations are iteratively adjusted in an attempt to make the output of the network mimic the observed property value.
Each of the four methods referenced above for predicting property values do not predict the value of a property with absolute accuracy. Each method is considered to operate within acceptable bounds of accuracy and each method is expected to predict the value of a property within a few percent, for example 5%, of an actual sale price and within a certain percentage of the time, for example 15% of the time. To improve the overall prediction accuracy from currently available prediction methods, the values predicted by these methods may be averaged. However, some methods have been determined to be more accurate than others. Accordingly, there is a need to improve the accuracy of a prediction based on an average value.